Exploring IFRS 9 Best Practices: Insights from Leading European Banks

June 2024
7 min read

A comprehensive summary of a recent webinar on diverse modelling techniques and shared challenges in expected credit losses

Across the whole of Europe, banks apply different techniques to model their IFRS9 Expected Credit Losses on a best estimate basis. The diverse spectrum of modelling techniques raises the question: what can we learn from each other, such that we all can improve our own IFRS 9 frameworks? For this purpose, Zanders hosted a webinar on the topic of IFRS 9 on the 29th of May 2024. This webinar was in the form of a panel discussion which was led by Martijn de Groot and tried to discuss the differences and similarities by covering four different topics. Each topic was discussed by one  panelist, who were Pieter de Boer (ABN AMRO, Netherlands), Tobia Fasciati (UBS, Switzerland), Dimitar Kiryazov (Santander, UK), and Jakob Lavröd (Handelsbanken, Sweden).

The webinar showed that there are significant differences with regards to current IFRS 9 issues between European banks. An example of this is the lingering effect of the COVID-19 pandemic, which is more prominent in some countries than others. We also saw that each bank is working on developing adaptable and resilient models to handle extreme economic scenarios, but that it remains a work in progress. Furthermore, the panel agreed on the fact that SICR remains a difficult metric to model, and, therefore, no significant changes are to be expected on SICR models.

Covid-19 and data quality

The first topic covered the COVID-19 period and data quality. The poll question revealed widespread issues with managing shifts in their IFRS 9 model resulting from the COVID-19 developments. Pieter highlighted that many banks, especially in the Netherlands, have to deal with distorted data due to (strong) government support measures. He said this resulted in large shifts of macroeconomic variables, but no significant change in the observed default rate. This caused the historical data not to be representative for the current economic environment and thereby distorting the relationship between economic drivers and credit risk. One possible solution is to exclude the COVID-19 period, but this will result in the loss of data. However, including the COVID-19 period has a significant impact on the modelling relations. He also touched on the inclusion of dummy variables, but the exact manner on how to do so remains difficult.

Dimitar echoed these concerns, which are also present in the UK. He proposed using the COVID-19 period as an out-of-sample validation to assess model performance without government interventions. He also talked about the problems with the boundaries of IFRS 9 models. Namely, he questioned whether models remain reliable when data exceeds extreme values. Furthermore, he mentioned it also has implications for stress testing, as COVID-19 is a real life stress scenario, and we might need to think about other modelling techniques, such as regime-switching models.

Jakob found the dummy variable approach interesting and also suggested the Kalman filter or a dummy variable that can change over time. He pointed out that we need to determine whether the long term trend is disturbed or if we can converge back to this trend. He also mentioned the need for a common data pipeline, which can also be used for IRB models. Pieter and Tobia agreed, but stressed that this is difficult since IFRS 9 models include macroeconomic variables and are typically more complex than IRB.

Significant Increase in Credit Risk

The second topic covered the significant increase in credit risk (SICR). Jakob discussed the complexity of assessing SICR and the lack of comprehensive guidance. He stressed the importance of looking at the origination, which could give an indication on the additional risk that can be sustained before deeming a SICR.

Tobia pointed out that it is very difficult to calibrate, and almost impossible to backtest SICR. Dimitar also touched on the subject and mentioned that the SICR remains an accounting concept that has significant implications for the P&L. The UK has very little regulations on this subject, and only requires banks to have sufficient staging criteria. Because of these reasons, he mentioned that he does not see the industry converging anytime soon. He said it is going to take regulators to incentivize banks to do so. Dimitar, Jakob, and Tobia also touched upon collective SICR, but all agreed this is difficult to do in practice.

Post Model Adjustments

The third topic covered post model adjustments (PMAs). The results from the poll question implied that most banks still have PMAs in place for their IFRS 9 provisions. Dimitar responded that the level of PMAs has mostly reverted back to the long term equilibrium in the UK. He stated that regulators are forcing banks to reevaluate PMAs by requiring them to identify the root cause. Next to this, banks are also required to have a strategy in place when these PMAs are reevaluated or retired, and how they should be integrated in the model risk management cycle. Dimitar further argued that before COVID-19, PMAs were solely used to account for idiosyncratic risk, but they stayed around for longer than anticipated. They were also used as a countercyclicality, which is unexpected since IFRS 9 estimations are considered to be procyclical. In the UK, banks are now building PMA frameworks which most likely will evolve over the coming years.

Jakob stressed that we should work with PMAs on a parameter level rather than on ECL level to ensure more precise adjustments. He also mentioned that it is important to look at what comes before the modelling, so the weights of the scenarios. At Handelsbanken, they first look at smaller portfolios with smaller modelling efforts. For the larger portfolios, PMAs tend to play less of a role. Pieter added that PMAs can be used to account for emerging risks, such as climate and environmental risks, that are not yet present in the data. He also stressed that it is difficult to find a balance between auditors, who prefer best estimate provisions, and the regulator, who prefers higher provisions.

Linking IFRS 9 with Stress Testing Models

The final topic links IFRS 9 and stress testing. The poll revealed that most participants use the same models for both. Tobia discussed that at UBS the IFRS 9 model was incorporated into their stress testing framework early on. He pointed out the flexibility when integrating forecasts of ECL in stress testing. Furthermore, he stated that IFRS 9 models could cope with stress given that the main challenge lies in the scenario definition. This is in contrast with others that have been arguing that IFRS 9 models potentially do not work well under stress. Tobia also mentioned that IFRS 9 stress testing and traditional stress testing need to have aligned assumptions before integrating both models in each other.

Jakob agreed and talked about the perfect foresight assumption, which suggests that there is no need for additional scenarios and just puts a weight of 100% on the stressed scenario. He also added that IFRS 9 requires a non-zero ECL, but a highly collateralized portfolio could result in zero ECL. Stress testing can help to obtain a loss somewhere in the portfolio, and gives valuable insights on identifying when you would take a loss. 

Pieter pointed out that IFRS 9 models differ in the number of macroeconomic variables typically used. When you are stress testing variables that are not present in your IFRS 9 model, this could become very complicated. He stressed that the purpose of both models is different, and therefore integrating both can be challenging. Dimitar said that the range of macroeconomic scenarios considered for IFRS 9 is not so far off from regulatory mandated stress scenarios in terms of severity. However, he agreed with Pieter that there are different types of recessions that you can choose to simulate through your IFRS 9 scenarios versus what a regulator has identified as systemic risk for an industry. He said you need to consider whether you are comfortable relying on your impairment models for that specific scenario.

This topic concluded the webinar on differences and similarities across European countries regarding IFRS 9. We would like to thank the panelists for the interesting discussion and insights, and the more than 100 participants for joining this webinar.

Interested to learn more? Contact Kasper Wijshoff, Michiel Harmsen or Polly Wong for questions on IFRS 9.

Model Risk Management​ – Expanding quantification of model risk

February 2024
7 min read

A comprehensive summary of a recent webinar on diverse modelling techniques and shared challenges in expected credit losses

Model risk from risk models has become a focal point of discussion between regulators and the banking industry. As financial institutions strive to enhance their model risk management practices, the need for robust model risk quantification becomes paramount.​​

An introduction to model risk quantification​

Many firms already have comprehensive model risk management frameworks that tier models using an ordinal rating (such as high/medium/low risk). However, this provides limited information on potential losses due to model risk or the capital cost of already identified model risks. Model risk quantification uses quantitative techniques to bridge this gap and calculate the potential impact of model risk on a business. ​

The goal of a model risk quantification framework​

As with many other sources of risk within a financial institute, the aim is to manage risk by holding capital against potential losses from the use of individual models across the firm. This can be achieved by including model risk as a component of Pillar 2 within the Internal Capital Adequacy Assessment Process (ICAAP).​

Key components of a quantification framework

An effective model risk quantification framework should be:​

  • Risk-based: By utilising model tiering results to identify models with risk worth the cost of quantifying.​
  • Process driven: By providing a system for identifying, measuring and classifying the impact of model risks.​
  • Aggregable: By producing results that can be aggregated and including a methodology for aggregating model results to a firm level.​
  • Transparent & capitalised: By regularly reporting aggregated firm-wide model risk and managing it using capitalisation.​
Blockers impeding model risk quantification

Complications of quantification include:​​

  • Implementation and running costs: Setting up and regularly running any quantification test involves significant resource costs. ​
  • Uncovered risk: Trying to quantify all potential model risk is a Sisyphean task.​
  • Internal resistance: Quantification and capitalisation of model risks will require increased resources to produce, leading to higher costs, making it a hard initiative to motivate individuals to follow.

Concepts in Model Risk Quantification​

Impacts of Model Risk

Model risk significantly influences financial institutions through valuations, capital requirements, and overall risk management strategies. The uncertainties tied to model outcomes can have profound impacts on regulatory compliance, economic capital, and the firm's standing in the financial ecosystem.​

Model tiering

Model tiering is a qualitative exercise that assesses the holistic risk of a model by considering various factors (e.g. materiality, importance, complexity, transparency, operational intricacies, and controls).​

The tiering output grades the risk of a model on an ordinal scale, comparing it to other models within the institute. However, it doesn't provide a quantitative metric that can be aggregated with other models.​

Overlap with quantitative regulations

Most firms already perform quantitative processes to measure the performance of Pillar 1 models that impact the regulatory capital held (such as the VaR backtesting multiplier applied to market risk RWA).​

Model Risk Quantification Framework​ - The Model Uncertainty Approach​

A crucial step in building a robust model risk quantification framework is classifying and assessing the impact of model risk. The model uncertainty approach is an internal quantitative approach in which model risks are identified and quantified on an individual level. Individual model risks are subsequently aggregated and translated into a monetary impact on the bank.   

​Regulatory Model Risk Quantificaiton Methods​ - RNIV, Backtesting Multiplier, Prudent Valuation and MoC​

Most banks are already familiar with quantification techniques recommend by regulators for risk management. Below we highlight some of these techniques that can be used as the basis for expansion of quantification within a firm. ​

Expanding Model Risk Quantification​

Our approach to efficient measurement relies on two key components. The first is model risk classifications to prioritize models to quantify, and the second is a knowledge base of already implemented regulatory and internally developed techniques to quantify that risk. This approach provides good risk coverage whilst also being extremely resource efficient.​

Looking to learn more about Model Risk Management? Reach out to our experts Dr. Andreas Peter, Alexander Mottram, Hisham Mirza.


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